ABSTRACT Short-term working memory is critical for all cognition. It is important to fluid intelligence by definition and is disordered in many psychiatric conditions. It is also an ideal model system for studying the link between the dynamics and functions of neural circuits. Short-term storage requires dynamics that are flexible enough to allow continuous incorporation of new information, yet stable enough to retain information for tens of seconds. Much is known about the neuronal substrate of short-term memory. There is a gap, however, in our knowledge of how neuronal resources are efficiently allocated to store multiple items. This gap is particularly striking given that a multi-item memory task (memory span task) is often used to measure fluid intelligence. Neurons in frontal areas are active during a memory period, and individual neurons are tuned to respond to particular memoranda. It is known that individual cells ramp up or down during a memory period. However, we were surprised to discover in preliminary experiments that 80% of individual cells in memory circuits lose their tuning before the end of a 15s memory period. This loss of tuning occurs at similar times across repeated trials; a neuron that loses tuning at 3s in one trial seldom remains tuned for more than 7s in a subsequent trial, and vice versa. This leads to the question of whether cells with common ?drop-out? times are linked together in a subnetwork, similar to the ?slot? organization often posited to support multi-item memory. We formulated a theory about how these subnetworks might be organized to enact a form of efficient resource allocation that balances demand for memory capacity against memory duration. The primary goal of this proposal is to test the validity of this theory, and more generally probe memory circuits for evidence of functional subnetworks, using a unique combination of long-delay multi-item memory tasks, computational modeling and analysis. In Aim 1, we will test a key facet of our theory: how storage of information may interact with the phenomenon of ?drop-out? to either help or hinder short-term memory storage. In Aims 2 and 3, we will test whether cells with similar dropout times also share other properties, as a way of determining whether they are in fact linked together in subnetworks. In parallel, we will develop modeling and optimization tools to ask how such subnetworks might be enacted in neural circuits, while also engaging the higher-level question of whether subnetworks are in fact a sensible solution to the problem of efficient resource allocation in the first place. A key aspect of the proposal is the integration of experimental and computational methods, including formalisms from information and control theories, so as to build tight links between (i) the observed phenomenology; (ii) the mathematical consistency of the theory; and (iii) how (i) and (ii) might be reconciled mechanistically in the dynamics of neural circuits. Together, these Aims have the potential to change the way we think about the neuronal substrate of short-term memory and how neural circuits are structured to best manage their resources.